Large-scale cortical networks estimated from scalp EEG signals during performance of goal-directed motor tasks.

F. De Vico Fallani, L. Astolfi, F. Cincotti, D. Mattia, A. G. Maglione, G. Vecchiato, J. Toppi, F. Della Penna, S. Salinari, F. Babiloni, G. Zouridakis

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The evaluation of the topological properties of brain networks is an emergent research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral "marker" for motor actions that result in successful reaching of a target.

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Electroencephalography
Scalp
Brain
Neuroimaging
Synchronization
Research

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Large-scale cortical networks estimated from scalp EEG signals during performance of goal-directed motor tasks.",
abstract = "The evaluation of the topological properties of brain networks is an emergent research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral {"}marker{"} for motor actions that result in successful reaching of a target.",
author = "{De Vico Fallani}, F. and L. Astolfi and F. Cincotti and D. Mattia and Maglione, {A. G.} and G. Vecchiato and J. Toppi and {Della Penna}, F. and S. Salinari and F. Babiloni and G. Zouridakis",
year = "2010",
language = "English",
pages = "1738--1741",
journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Large-scale cortical networks estimated from scalp EEG signals during performance of goal-directed motor tasks.

AU - De Vico Fallani, F.

AU - Astolfi, L.

AU - Cincotti, F.

AU - Mattia, D.

AU - Maglione, A. G.

AU - Vecchiato, G.

AU - Toppi, J.

AU - Della Penna, F.

AU - Salinari, S.

AU - Babiloni, F.

AU - Zouridakis, G.

PY - 2010

Y1 - 2010

N2 - The evaluation of the topological properties of brain networks is an emergent research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral "marker" for motor actions that result in successful reaching of a target.

AB - The evaluation of the topological properties of brain networks is an emergent research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral "marker" for motor actions that result in successful reaching of a target.

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